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. 2016 Mar 10;11(3):e0150705.
doi: 10.1371/journal.pone.0150705. eCollection 2016.

Changes in the miRNA-mRNA Regulatory Network Precede Motor Symptoms in a Mouse Model of Multiple System Atrophy: Clinical Implications

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Free PMC article

Changes in the miRNA-mRNA Regulatory Network Precede Motor Symptoms in a Mouse Model of Multiple System Atrophy: Clinical Implications

Simon Schafferer et al. PLoS One. .
Free PMC article

Abstract

Multiple system atrophy (MSA) is a fatal rapidly progressive α-synucleinopathy, characterized by α-synuclein accumulation in oligodendrocytes. It is accepted that the pathological α-synuclein accumulation in the brain of MSA patients plays a leading role in the disease process, but little is known about the events in the early stages of the disease. In this study we aimed to define potential roles of the miRNA-mRNA regulatory network in the early pre-motor stages of the disease, i.e., downstream of α-synuclein accumulation in oligodendroglia, as assessed in a transgenic mouse model of MSA. We investigated the expression patterns of miRNAs and their mRNA targets in substantia nigra (SN) and striatum, two brain regions that undergo neurodegeneration at a later stage in the MSA model, by microarray and RNA-seq analysis, respectively. Analysis was performed at a time point when α-synuclein accumulation was already present in oligodendrocytes at neuropathological examination, but no neuronal loss nor deficits of motor function had yet occurred. Our data provide a first evidence for the leading role of gene dysregulation associated with deficits in immune and inflammatory responses in the very early, non-symptomatic disease stages of MSA. While dysfunctional homeostasis and oxidative stress were prominent in SN in the early stages of MSA, in striatum differential gene expression in the non-symptomatic phase was linked to oligodendroglial dysfunction, disturbed protein handling, lipid metabolism, transmembrane transport and altered cell death control, respectively. A large number of putative miRNA-mRNAs interaction partners were identified in relation to the control of these processes in the MSA model. Our results support the role of early changes in the miRNA-mRNA regulatory network in the pathogenesis of MSA preceding the clinical onset of the disease. The findings thus contribute to understanding the disease process and are likely to pave the way towards identifying disease biomarkers for early diagnosis of MSA.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Neuropathological and behavioral characterization of a mouse model of a pre-motor stage of MSA.
(A) Human α-synuclein overexpression in MSA transgenic mice resulted in α-synuclein accumulation in oligodendrocytes (arrows) detectable both in substantia nigra and striatum. (B) No dopaminergic neuronal loss was identified in the pre-motor stage in substantia nigra of MSA mice (n = 6) as compared to controls (n = 4) by stereological determination of the number of tyrosine hydroxylase (TH)-immunoreactrive (IR) neurons. (C) No GABAergic medium spiny neurons loss was identified in the pre-motor stage in striatum of MSA mice (n = 6) as compared to controls (n = 4) by stereological determination of the number of DARPP-32-IR neurons. (D) Iba-1-IR was used to determine the number and activation status of microglia (type A, B, C, and D [29]) in MSA (n = 3) and control mice (n = 3). No significant differences were detected between the groups with predominant representation of type A resting microglia in both substantia nigra and striatum. (E) GFAP-immunohistochemistry was used to determine the level of astroglial activation in MSA (n = 5) and control mice (n = 3) in substantia nigra and striatum. No significant differences were identified between the groups. Statistical analysis of the neuropathological data to compare control and transgenic MSA mice was done by t-test analysis with GraphPad Prism 5.03 software. Statistical significance was set at p<0.05. Data are presented as mean ± SEM. (F) TUNEL staining detected no cell death in SN and striatum of PM3 MSA mice. As a positive control we applied aged PM12 MSA mice (an age when detectable neuronal loss is recorded) that demonstrated positive TUNEL staining.
Fig 2
Fig 2. Differential expression of mRNAs in a mouse model of pre-motor stage MSA.
(A) Heatmaps represent significantly differentially expressed genes of RNA-seq (striatum, SN) and microarray (SN) analyses. For each gene (row), the log2-transformed change of the expression value in each sample to the average expression value over all samples is shown. Columns represent individual replicates grouped into MSA and control (WT) samples indicated by the blue (MSA) and grey (WT) bars at the top of the heatmaps. The color gradient indicates the expression change from negative to positive. The asterisks following gene names indicate overlapping genes between microarray and RNA-seq analyses in SN. (B) Venn diagram illustrating the number of overlapping differentially expressed mRNAs between SN and striatum tissue in MSA mice. (C) Heatmap highlights log2-transformed fold changes of mRNAs overlapping between striatum und SN. Down-regulated mRNAs are indicated by a blue color gradient, whereas up-regulated miRNAs are indicated by an orange color gradient. mRNA with expression signals below background in the microarray experiment are highlighted in gray. From left to right, microarray and RNA-seq analysis results of SN and RNA-seq analysis of striatum are shown. Differential expression analysis of control versus transgenic MSA mice of both, striatum and SN samples, was performed by employing the DESeq2 package with predefined parameters [37]. Genes with an adjusted p-value below 0.1 after multiple testing corrections were considered statistically significant [38]. For microarray data differential gene expression was tested by a moderated t-test using the limma package [39]. For both methods genes with an adjusted p-value < 0.1 after multiple testing corrections were considered statistically significant [38].
Fig 3
Fig 3. Differential expression of miRNAs in a mouse model of pre-motor stage MSA.
(A) Heatmap shows expression changes of miRNAs of striatum (left) and SN (right). miRNAs with statistically significant (adjusted p<0.1) changes are indicated by a red line on the side. Gray boxes designate miRNAs with expression signals below background. The color gradient shows positive and negative log2-transformed fold changes in orange and blue color, respectively. (B) Fold change and adjusted p-value of the miRNAs of the mir-467 family. (C) Venn diagram illustrates the overlap of differentially expressed miRNAs between SN and striatum in MSA mice. Differential expression analysis was performed by calculating a linear model for each miRNA according to the guidelines for simple dye swap experiments [39]. Duplicated spots were considered in the linear model fit. This model was then employed to obtain test statistics by the empirical Bayes method providing stable estimations for the sample variance of a small number of arrays [44]. All differentially expressed miRNAs with an adjusted p-value < 0.1 after multiple testing corrections as proposed by Benjamini and Hochberg were considered statistically significant [38].
Fig 4
Fig 4
De-regulated miRNAs and their correlation to putative target de-regulated mRNAs in substantia nigra (A) and striatum (B) of MSA mice in a pre-motor stage of disease. Shortlisted miRNA-targets are based on 3 factors: (i) predicted in miRwalk with p-value<0.1 (ii) experimentally validated and (iii) present in at least two prediction programs.
Fig 5
Fig 5. Deregulated miRNA-mRNA regulatory network to “Immune system process” in MSA mice in disease pre-motor stage.
Differentially expressed miRNAs with predicted negatively correlated differentially expressed mRNA targets are visualized by employing Cytoscape (version 3.2.1). Round nodes show mRNA and triangle nodes miRNA. Node size is proportional to its degree. Fold change (log2 transformed) for each node is ranging from red (negative) to green (positive). Interaction arrow thickness is proportional to the number of algorithms predicting the miRNA-mRNA target 3’ UTR interaction, ranging from one to four. Differential expression of genes, in striatum and SN, such as Anln, Car2, Cd59a, Hba-a1 and Rps17, is visualized by color corresponding to the mean fold change (exact values can be found in S2 and S3 Tables).
Fig 6
Fig 6. Deregulated miRNA-mRNA regulatory network to “Homeostasis/oxidative stress” in SN of MSA mice in a pre-motor stage.
Differentially expressed miRNAs with predicted negatively mRNA targets assigned to the indicated GO-terms (light blue rectangles) are visualized by employing Cytoscape (version 3.2.1). Round nodes designate mRNA and triangle nodes miRNA. Node size is proportional to its degree. Fold change (log2 transformed) for each node is ranging from -0.75 (red) to 1 (green). The shade of blue color of the interaction arrows indicates the degree (range -1.00–0.00) of negative correlation between miRNA-mRNA target 3’ UTR interaction. Interaction arrow thickness is proportional to the number of algorithms predicting the miRNA-mRNA target 3’ UTR interaction, ranging from one to four.
Fig 7
Fig 7
Deregulated miRNA-mRNA regulatory network in the striatum of MSA mice in pre-motor stage of disease: Modules “Protein handling” (A) and “Metabolism” (B). Differentially expressed miRNAs with predicted negatively correlated differentially expressed mRNA targets assigned to the indicated GO-terms (light blue rectangles) are visualized by employing Cytoscape (version 3.2.1). Round nodes designate mRNA and triangle nodes miRNA. Node size is proportional to its degree. Fold change (log2 transformed) for each node is ranging from -0.75 (red) to 1 (green). The shade of blue color of the interaction arrows indicates the degree (range -1.00–0.00) of negative correlation between miRNA-mRNA target 3’ UTR interaction. Interaction arrow thickness is proportional to the number of algorithms predicting the miRNA-mRNA target 3’ UTR interaction, ranging from one to four.
Fig 8
Fig 8
Deregulated miRNA-mRNA regulatory network in the striatum of MSA mice in pre-motor stage of disease: Modules “Transmembrane transport” (A) and “Cell death” (B). Differentially expressed miRNAs with predicted negatively correlated differentially expressed mRNA targets assigned to the indicated GO-terms (light blue rectangles) are visualized by employing Cytoscape (version 3.2.1). Round nodes designate mRNA and triangle nodes miRNA. Node size is proportional to its degree. Fold change (log2 transformed) for each node is ranging from -0.75 (red) to 1 (green). The shade of blue color of the interaction arrows indicates the degree (range -1.00–0.00) of negative correlation between miRNA-mRNA target 3’ UTR interaction. Interaction arrow thickness is proportional to the number of algorithms predicting the miRNA-mRNA target 3’ UTR interaction, ranging from one to four.

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